Acoustic emissions, or stresswaves, arise in an article due to many different types of events, for example impacts of loose components on the article, a sudden movement of a defect, such as a crack, in the article or a sudden movement of an unbonded region between two joined components forming the article, by fibre breakage, matrix cracking and ply delaminations of composite material articles.
Previously sources of acoustic emission have been located by measuring the time of arrival of an acoustic emission pulse at each of several transducers. The difference in the times of arrival of the acoustic emission pulse at each of the transducers is calculated and triangulation techniques are used to deduce the location of the source of the acoustic emissions. The triangulation technique is generally performed analytically using complex, derived equations or by using look-up tables and interpolating. These derived equations are very difficult to derive for composite material articles or other articles with complex structures, because the relative velocity of sound in every direction must be calculated, i.e. composite material articles are anisotropic. This means that locating a source of acoustic emissions using triangulation techniques has to be set up by an acoustic emission expert. Commercially available systems for locating sources of acoustic emission are restricted to simple geometries such as flat plates, cylinders and spheres.
It is known to use neural networks to calculate the position of the source of an acoustic emission from published European Patent Application No 0482750A1 published Apr. 29, 1992. This document discloses measuring the times for the electrical output signals from each of the transducers to exceed two predetermined amplitudes from a datum time for artificially induced acoustic emission events having known locations to infer the mathematical relationship between values of time and location of the acoustic emission event. The times taken for the electrical output signals from the transducers to exceed the two predetermined amplitudes for an acoustic emission event of unknown location are measured and the neural network uses the inferred mathematical relationship to calculate the location of the unknown source.
It is known to artificially induce acoustic emissions by breaking a pencil lead against the article or by directing a laser beam onto the surface of the article.
A problem with the apparatus using neural networks for locating a source of acoustic emissions in an article is that they require an acoustic emission experts to set up and teach the neural network to infer the mathematical relationship between values of time and location of the acoustic emission event.